Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data is challenging.

So, if you are a senior leader in a Fortune 2000 company. How do you structure your group to deliver effective BI, Analytics or Big Data projects? Do you have the right structure, toolset, dataset, skillset and mindset for analytics and Big Data?

Organizing for effective BI, Analytics and Big Data is becoming a hot topic in corporations. In 2012, business users are exerting significant influence over BI, Analytics and Big Data decisions, often choosing analytics and visualization platforms and products in addition to/as alternatives to traditional BI platform (reporting and visualization tools).

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Data-driven DNA is about having the right toolset, mindset, skillset and dataset to evolve a major brand and seize today’s omni-channel opportunities. Whether it’s retooling and retraining for the multiscreen attention economy, or introducing digital innovations that transform both retail and healthcare, P&G is bringing data into every part of its core strategies to fight for the customer.

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Striving for market leadership in consumer products is a non-stop managerial quest. In the struggle for survival, the fittest win out at the expense of their rivals because they succeed in adapting themselves best to their environment.

CMOs and CIOs everywhere agree that analytics is essential to sales & marketing and that its primary purpose is to gain access to customer insight and intelligence along the market funnel – awareness, consideration, preference, purchase and loyalty.

In this posting we illustrate a best-in-class “run-the-business” with Data/Analytics Case Study at P&G. The case study demonstrates four key characteristics of data market leaders:

A shared belief that data is a core asset that can be used to enhance operations, customer ser­vice, marketing and strategy

More effective leverage of more data – corporate, product, channel, and customer – for faster results

Technology is only a tool, it is not the answer..!

Support for analytics by senior managers who embrace new ideas and are willing to shift power and resources to those who make data-driven decisions

This case study of a novel construct called Business Cockpit (also called LaunchTower in the Biotech and Pharmaceutical Industry) illustrates the way Business Analytics is becoming more central in retail and CPG decision making.

Here is a quick summary of P&G Analytics program:

Primary focus on improving management decisions at scale – did the analysis to identify time gap between information and application to decision making

P&G Overview

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“Running a company is an endless quest to find out things you don’t know“

– Jeff Immelt, CEO GE

What will 2012 bring? Recently, I attended the CIO Executive Leadership Summit in Greenwich, Connecticut. I was particularly intrigued by the presentation by the new CIO of IBM, Jeanette Horan where she presented the projects she was tackling and how IBM is thinking about business analytics.

IBM is making a bet that “true leaders” will develop the capabilities required for making good and timely decisions in unpredictable and stressful environments.

IBM is adapting to this new data analytics reality by a rapid-fire acquisition strategy: Cognos, Netezza, SPSS, ILog, CoreMetrics, Algorithmics, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail). IBM also has other information management assets like Watson, DB2 etc. They are building a formidable capability around the value chain: “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” . They see this as a $20Bln opportunity. Read more

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The “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions” is a differentiating causal chain in business today. To service this “data->decision” chain a very large industry is emerging.

But the interest in BI and analytics is surging. Arnab Gupta, CEO of Opera states why analytics are taking center stage, “We live in a world where computers, not people, are in the driver’s seat. In banking, virtually 100% of the credit decisions are made by machines. In marketing, advanced algorithms determine messages, sales channels, and products for each consumer. Online, more and more volume is spurred by sophisticated recommender engines. At Amazon.com, 40% of business comes from its “other people like you bought…” program.” (Businessweek, September 29, 2009).

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The term “business intelligence” (BI) dates back to 1958, when IBM researcher Hans Peter Luhn coined the term in an IBM Journal article.

However, it took until 1980s when decision support systems (DSS) became popular and mid 1990s for BI started to emerge as an umbrella term to cover software-enabled innovations in performance management, planning, reporting, querying, analytics, online analytical processing, integration with operational systems, predictive analytics and related areas.

Gartner 2014 magic quadrant shows the key players in the BI market. The different players are differentiated based on five abilities— ability to handle large volumes of data, ability to deal with data velocity, variety (structured and unstructured), visualization capabilities and domain/vertical specific accelerators.

Market Evolution

Analytics is becoming three different markets. First of all, there is the BI market which is actually going through quite a bit of change itself. This is a more consolidated market than we have seen in the past and there is a tremendous amount of work being done by Oracle, SAP, IBM and others to kind of retool it for the next generation of BI. So it is a growing market, lots of upgrade, replatform, modernization demand, lots of clients who are finally realizing that the tools (visualization etc.) are ready to give them some of the capability that they have historically cared about.

The second part of the market is what is called Advanced Analytics. Here you need PhD level data scientists who have backgrounds in machine learning, industry specific domain modeling, and different types of data science who can apply that in a very specific way to specific industry problems. This is a rapidly growing part of IT Services. Also, there are just not enough data scientists to go around.

The third part of the market is Analytics as a Service. This is about leveraging software-as-a-service platforms as opposed to on-premise. This is about a business model that is more like Business Process Outsourcing (BPO). Clients buy business outcomes; they don’t buy transactions and FTEs.

The analytics market has thousands of boutique consultants who are specialists in particular industries or specific technologies. It includes all the major technology providers, who are all trying to advance their business and capabilities that they are bringing to the market. And then there are vendors who are just bringing sheer capacity of data science skills to the market and they are coming in from a completely different angle of basically just renting the expertise of their data scientists into the market.

The market is incredibly fragmented. We are in the early stages of growth in the market. Every single one of our clients is building this capability internally and they are looking for more services from vendors, because the opportunity to apply analytics is in every single one function whether it is a customer analytics, industrial Internet, e-commerce platform, is growing. Analytics is embedded into literally every single business interaction.

BI, Analytics [and Big Data] Market Sizing

More recently to support a new generation of cost cutting and growth initiatives, corporations are investing heavily to gain near real-time actionable insights (historical and predictive), and from a mix of disparate spreadsheets and myriad of systems (legacy, internal silos, customer facing, suppliers, partners, etc.).

Defining Business Analytics

What is Business Analytics? Business Analytics is the intersection of business and technology, offering new opportunities for a competitive advantage. Business analytics unlocks the predictive potential of data analysis to improve financial performance, strategic management, and operational efficiency.

What is BI? BI is the "computer-based techniques used in spotting, digging-out, and analyzing 'hard' business data, such as sales revenue by products or departments or associated costs and incomes. Objectives of BI implementations include (1) understanding of a firm's internal and external strengths and weaknesses, (2) understanding of the relationship between different data for better decision making, (3) detection of opportunities for innovation, and (4) cost reduction and optimal deployment of resources." (Business Dictionary). Most widely used BI tool is Microsoft Excel.
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What is Big Data? Big data refer to data scenarios that grow so large (petabytes and more) that they become awkward to work with using traditional database management tools. The challenge stems from data volume + flow velocity + noise to signal conversion. Big data is spawning new tools that are mix of significant processing power, parallelism and statistical, machine learning, or pattern recognition techniques
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Corporate performance management software and performance management concepts, such as the balanced scorecard, enable organizations to measure business results and track their progress against business goals in order to improve financial performance.
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Data visualization tools, include mashups, executive dashboards, performance scorecards and other data visualization technology, is becoming a major category.
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BI platforms provide a range of capabilities for building analytical applications. Examples are Oracle OBIEE, SAP Business Objects 4.0. There are many choices and combinations of BI platforms, capabilities and use cases as well as many emerging BI technologies such as in memory analytics, interactive visualization and BI integrated search. The idea of standardizing on one supplier for all of one’s BI capabilities is difficult to do. Increasingly, standardization and more about managing a portfolio of tools used for a set of capabilities and use cases.
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Data integration tools and architectures in support of BI continue to evolve. Extract-Transfer-Load (ETL) tools make up a big segment of this category in addition to data mapping tools. Organizations must now support a range of delivery styles, latencies, and formats.
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BI is about "sense and respond." Analytics is about "anticipate and shape" models.

About

Business Analytics 3.0 blog is meant for decision makers and managers who are trying to make sense of the rapidly changing technology landscape and build next generation solutions. It is aimed at helping business decision makers navigate the "Raw Data -> Aggregate Data -> Intelligence -> Insight -> Decisions" chain.